Construction of a hypoxia-immune-related prognostic model and targeted therapeutic strategies for cervical cancer

Abstract

Emerging evidence indicates that hypoxia and immunity play important roles in tumorigenesis and development. However, the hypoxia-immune-related prognostic risk model has not been established in cervical cancer (CC). We aimed to construct a hypoxia-immune-related prognostic risk model, which has potential application in predicting the prognosis of CC patients and the response to targeted therapy. The RNA-seq data and corresponding clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. The hypoxia status and immune status of CC patients were evaluated using the Consensus Clustering method and single-sample gene set enrichment analysis (ssGSEA), respectively. The univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were applied to establish the prognostic risk model of CC. The chemotherapy response for six chemotherapeutic agents of each CC patient was calculated according to the Genomics of Drug Sensitivity in Cancer (GDSC). And the Connectivity Map (CMap) database was performed to screen candidate small-molecule drugs. In this study, we identified seven gene signatures (P4HA2, MSMO1, EGLN1, ZNF316, IKZF3, ISCU and MYO1B) with prognostic values. And the survival time of patients with low risk was significantly longer than those with high risk. Meanwhile, CC patients in the high-risk group yielded higher sensitivity to five chemotherapeutic agents. And we listed 10 candidate small-molecule drugs that exhibited a high correlation with the prognosis of CC. Thus, the prognostic model can accurately predict the prognosis of patients with CC and may be helpful for the development of new hypoxia-immune prognostic markers and therapeutic strategies for CC.

Keywords: hypoxia-immune-related gene signatures; prediction; prognosis; therapy; tumor microenviroment.